CN112785714A

CN112785714A - Point cloud instance labeling method and device, electronic equipment and medium

Info

Publication number: CN112785714A
Application number: CN202110129685.XA
Authority: CN
Inventors: 于帅帅
Original assignee: Beijing Baidu Netcom Science and Technology Co Ltd
Current assignee: Beijing Baidu Netcom Science and Technology Co Ltd
Priority date: 2021-01-29
Filing date: 2021-01-29
Publication date: 2021-05-11

Abstract

The disclosure provides a point cloud instance labeling method, a point cloud instance labeling device, electronic equipment, a computer readable storage medium and a computer program product, relates to the technical field of computers, particularly relates to the technical field of data labeling and computer vision, and can be applied to a cloud platform. The implementation scheme is as follows: acquiring point cloud data to be marked; labeling one or more instances on the point cloud data, wherein each instance comprises one or more point cloud areas in the point cloud data; and determining attributes of the one or more instances, wherein the attributes are one or more of a set of attributes preset based on the scene corresponding to the point cloud.

Description

Point cloud instance labeling method and device, electronic equipment and medium

Technical Field

The present disclosure relates to the field of computer technologies, and in particular, to the field of data annotation and computer vision technologies, and in particular, to a method and an apparatus for point cloud instance annotation, an electronic device, a computer-readable storage medium, and a computer program product.

Background

Artificial intelligence is the subject of research that makes computers simulate some human mental processes and intelligent behaviors (such as learning, reasoning, thinking, planning, etc.), both at the hardware level and at the software level. The artificial intelligence hardware technology generally comprises technologies such as a sensor, a special artificial intelligence chip, cloud computing, distributed storage, big data processing and the like, and the artificial intelligence software technology mainly comprises a computer vision technology, a voice recognition technology, a natural language processing technology, machine learning/deep learning, a big data processing technology, a knowledge graph technology and the like.

To train a suitable model, the data needs to be labeled in advance. The data labeling requirements comprise point cloud instance segmentation labeling requirements, such as point cloud lane line instance segmentation and point cloud obstacle instance segmentation, and corresponding category labels need to be given to target points in the point cloud; at the same time, different individuals of the same class need to be distinguished. Generally, the cut areas are classified and instance labeled by saving the file names. Because the name of the annotator is manual typing input, the problems of duplicate name coverage, wrongly written characters and the like exist, and the annotation quality is influenced.

Disclosure of Invention

The disclosure provides a point cloud instance labeling method, a point cloud instance labeling device, an electronic device, a computer readable storage medium and a computer program product.

According to an aspect of the present disclosure, there is provided a point cloud instance labeling method, including: acquiring point cloud data to be marked; labeling one or more instances on the point cloud data, wherein each instance comprises one or more point cloud areas in the point cloud data; and determining attributes of the one or more instances, wherein the attributes are one or more of a set of attributes preset based on the scene corresponding to the point cloud.

According to another aspect of the present disclosure, there is provided a point cloud instance annotation device, including: the acquisition unit is configured to acquire point cloud data to be marked; the annotation unit is configured to annotate one or more instances on the point cloud data, wherein each instance comprises one or more point cloud areas in the point cloud data; and the determining unit is configured to determine attributes of the one or more instances, wherein the attributes are one or more of a set of attributes preset based on the scene corresponding to the point cloud.

According to another aspect of the present disclosure, there is provided an electronic device including: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform a point cloud instance annotation process.

According to another aspect of the disclosure, a non-transitory computer-readable storage medium having computer instructions stored thereon for causing a computer to perform a point cloud instance annotation method is provided.

According to another aspect of the disclosure, a computer program product is provided, comprising a computer program, wherein the computer program, when executed by a processor, implements a point cloud instance annotation method.

According to one or more embodiments of the disclosure, through a set of attributes preset based on a scene corresponding to a point cloud, problems of duplicate names, wrongly written characters and the like caused by manual input by a marking person are solved, marking quality is improved, and the method has strong applicability.

It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the embodiments and, together with the description, serve to explain the exemplary implementations of the embodiments. The illustrated embodiments are for purposes of illustration only and do not limit the scope of the claims. Throughout the drawings, identical reference numbers designate similar, but not necessarily identical, elements.

FIG. 1 illustrates a schematic diagram of an exemplary system in which various methods described herein may be implemented, according to an embodiment of the present disclosure;

FIGS. 2A and 2B illustrate an original scene and a schematic diagram of a three-dimensional point cloud of the original scene, respectively, of an exemplary embodiment;

FIG. 3 shows a flow diagram of a point cloud instance annotation method according to an embodiment of the disclosure;

FIG. 4 shows a block diagram of a point cloud instance annotation device, in accordance with an embodiment of the present disclosure; and

FIG. 5 illustrates a block diagram of an exemplary electronic device that can be used to implement embodiments of the present disclosure.

Detailed Description

Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which various details of the embodiments of the disclosure are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.

In the present disclosure, unless otherwise specified, the use of the terms "first", "second", etc. to describe various elements is not intended to limit the positional relationship, the timing relationship, or the importance relationship of the elements, and such terms are used only to distinguish one element from another. In some examples, a first element and a second element may refer to the same instance of the element, and in some cases, based on the context, they may also refer to different instances.

The terminology used in the description of the various described examples in this disclosure is for the purpose of describing particular examples only and is not intended to be limiting. Unless the context clearly indicates otherwise, if the number of elements is not specifically limited, the elements may be one or more. Furthermore, the term "and/or" as used in this disclosure is intended to encompass any and all possible combinations of the listed items.

Embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.

Fig. 1 illustrates a schematic diagram of an exemplary system 100 in which various methods and apparatus described herein may be implemented in accordance with embodiments of the present disclosure. Referring to fig. 1, the system 100 includes one or

more client devices

101, 102, 103, 104, 105, and 106, a server 120, and one or more communication networks 110 coupling the one or more client devices to the server 120.

Client devices

101, 102, 103, 104, 105, and 106 may be configured to execute one or more applications.

In embodiments of the present disclosure, the server 120 may run one or more services or software applications that enable the point cloud instance annotation method to be performed.

In some embodiments, the server 120 may also provide other services or software applications that may include non-virtual environments and virtual environments. In certain embodiments, these services may be provided as web-based services or cloud services, for example, provided to users of

client devices

101, 102, 103, 104, 105, and/or 106 under a software as a service (SaaS) model.

In the configuration shown in fig. 1, server 120 may include one or more components that implement the functions performed by server 120. These components may include software components, hardware components, or a combination thereof, which may be executed by one or more processors. A user operating a

client device

101, 102, 103, 104, 105, and/or 106 may, in turn, utilize one or more client applications to interact with the server 120 to take advantage of the services provided by these components. It should be understood that a variety of different system configurations are possible, which may differ from system 100. Accordingly, fig. 1 is one example of a system for implementing the various methods described herein and is not intended to be limiting.

The

client devices

101, 102, 103, 104, 105, and/or 106 can be used to receive point cloud data to be annotated or to display a point cloud instance annotation process, and the like. The client device may provide an interface that enables a user of the client device to interact with the client device. The client device may also output information to the user via the interface. Although fig. 1 depicts only six client devices, those skilled in the art will appreciate that any number of client devices may be supported by the present disclosure.

Client devices

101, 102, 103, 104, 105, and/or 106 may include various types of computer devices, such as portable handheld devices, general purpose computers (such as personal computers and laptop computers), workstation computers, wearable devices, gaming systems, thin clients, various messaging devices, sensors or other sensing devices, and so forth. These computer devices may run various types and versions of software applications and operating systems, such as Microsoft Windows, Apple iOS, UNIX-like operating systems, Linux, or Linux-like operating systems (e.g., Google Chrome OS); or include various Mobile operating systems, such as Microsoft Windows Mobile OS, iOS, Windows Phone, Android. Portable handheld devices may include cellular telephones, smart phones, tablets, Personal Digital Assistants (PDAs), and the like. Wearable devices may include head mounted displays and other devices. The gaming system may include a variety of handheld gaming devices, internet-enabled gaming devices, and the like. The client device is capable of executing a variety of different applications, such as various Internet-related applications, communication applications (e.g., email applications), Short Message Service (SMS) applications, and may use a variety of communication protocols.

Network 110 may be any type of network known to those skilled in the art that may support data communications using any of a variety of available protocols, including but not limited to TCP/IP, SNA, IPX, etc. By way of example only, one or more networks 110 may be a Local Area Network (LAN), an ethernet-based network, a token ring, a Wide Area Network (WAN), the internet, a virtual network, a Virtual Private Network (VPN), an intranet, an extranet, a Public Switched Telephone Network (PSTN), an infrared network, a wireless network (e.g., bluetooth, WIFI), and/or any combination of these and/or other networks.

The server 120 may include one or more general purpose computers, special purpose server computers (e.g., PC (personal computer) servers, UNIX servers, mid-end servers), blade servers, mainframe computers, server clusters, or any other suitable arrangement and/or combination. The server 120 may include one or more virtual machines running a virtual operating system, or other computing architecture involving virtualization (e.g., one or more flexible pools of logical storage that may be virtualized to maintain virtual storage for the server). In various embodiments, the server 120 may run one or more services or software applications that provide the functionality described below.

The computing units in server 120 may run one or more operating systems including any of the operating systems described above, as well as any commercially available server operating systems. The server 120 may also run any of a variety of additional server applications and/or middle tier applications, including HTTP servers, FTP servers, CGI servers, JAVA servers, database servers, and the like.

In some implementations, the server 120 may include one or more applications to analyze and consolidate data feeds and/or event updates received from users of the

client devices

101, 102, 103, 104, 105, and 106. Server 120 may also include one or more applications to display data feeds and/or real-time events via one or more display devices of

client devices

101, 102, 103, 104, 105, and 106.

In some embodiments, the server 120 may be a server of a distributed system, or a server incorporating a blockchain. The server 120 may also be a cloud server, or a smart cloud computing server or a smart cloud host with artificial intelligence technology. The cloud Server is a host product in a cloud computing service system, and is used for solving the defects of high management difficulty and weak service expansibility in the traditional physical host and Virtual Private Server (VPS) service.

The system 100 may also include one or more databases 130. In some embodiments, these databases may be used to store data and other information. For example, one or more of the databases 130 may be used to store information such as point cloud data. The data store 130 may reside in various locations. For example, the data store used by the server 120 may be local to the server 120, or may be remote from the server 120 and may communicate with the server 120 via a network-based or dedicated connection. The data store 130 may be of different types. In certain embodiments, the data store used by the server 120 may be a database, such as a relational database. One or more of these databases may store, update, and retrieve data to and from the database in response to the command.

In some embodiments, one or more of the databases 130 may also be used by applications to store application data. The databases used by the application may be different types of databases, such as key-value stores, object stores, or regular stores supported by a file system.

The system 100 of fig. 1 may be configured and operated in various ways to enable application of the various methods and apparatus described in accordance with the present disclosure.

The Point Cloud is a massive Point set which expresses target space distribution and target surface characteristics under the same space reference system, and after the space coordinates of each sampling Point on the surface of the object are obtained, the obtained Point set is called as the Point Cloud. By way of example only, FIG. 2A is an original scene and FIG. 2B is a three-dimensional point cloud of the scene of FIG. 2A.

Point cloud segmentation is an important step in point cloud data processing, and in general, when a scanner performs three-dimensional scanning, the scanning range is long, the density of points is large, and even if a scanning area is selected, points outside a target scene are scanned. Generally, the point cloud data processing process occupies a memory space relatively, and in order to improve the data processing efficiency, the region of interest is divided and processed. In addition, in the scanning process, due to the disturbance of wind or the problems of shielding objects existing in a target scene, limited scanner precision and the like, flying spots and noise points exist in the point cloud data obtained by scanning. Some flying spots and noise spots can be segmented and deleted as special examples.

Generally, the cut areas can be classified and instantiated by saving the file names. Because the name of the annotator is manual typing input, the problems of duplicate name coverage, wrongly written characters and the like exist, and the annotation quality is influenced.

Therefore, according to an embodiment of the present disclosure, there is provided a point cloud instance annotation method 300, as shown in fig. 3, including: acquiring point cloud data to be marked (step 310); labeling one or more instances on the point cloud data, wherein each instance comprises one or more point cloud regions in the point cloud data (step 320); and determining attributes of the one or more instances, wherein the attributes are one or more of a set of attributes preset based on the scene corresponding to the point cloud (step 330).

According to the embodiment of the disclosure, the problems of duplicate names, wrongly written characters and the like caused by manual input of a marking person are solved through a set of attributes preset based on the scene corresponding to the point cloud, so that the marking quality is improved, and the method has strong applicability.

In some embodiments, the point cloud data to be annotated obtained by the client may be obtained in real time, or the point cloud data to be annotated may be obtained from a preset storage area. For example, the preset storage area may be a local storage area for pre-storing point cloud data to be annotated, or may be a database or a memory for storing the point cloud data to be annotated at a server. However, it should be understood that other methods of obtaining point cloud data to be annotated are possible and are not limited herein.

In some embodiments, the client may also be a sensor, such as an onboard sensor in the field of unmanned driving. The sensors acquire point cloud data of the vehicle surroundings and transmit the point cloud data to the server for storage and annotation. The examples to be labeled in the point cloud data may be, for example, obstacles such as cars, railings, pedestrians, trees, or billboards, and may also be lane lines on a driving road, which is not limited herein.

It should be understood that the above-mentioned point cloud data to be labeled is only an example, and the point cloud data to be labeled may be two-dimensional or three-dimensional data obtained from various application scenarios, such as the field of automatic driving, the field of face recognition, the field of motion sensing games, and the like. The point cloud data to be marked is not limited in the disclosure, and the point cloud data under various scenes is possible.

According to some embodiments, the attributes of an instance may include a name (i.e., an instance name). The name may further include: a primary name, a secondary name, and one or more secondary names may be included under the primary name. Illustratively, the second name may be a subdivision feature name of the annotation instance. For example, in a point cloud lane line instance labeling scenario, the labeled instance may include a name: solid lines, and may further include names: single solid line or double solid line, etc. By setting a multi-level name, the marked instances and their subdivision characteristics can be further identified and distinguished.

It is to be understood that, in the dot cloud example labeling, only the primary name thereof may be determined, or only the secondary name thereof may be determined, or both the primary name and the secondary name thereof may be determined, which is not limited herein.

According to some embodiments, instances determined to be the same name have the same visual attribute. According to some embodiments, the visual attribute comprises a color. For example, in a point cloud lane line instance labeling scenario, the instance labeled as a solid line may be red, the instance labeled as a dashed line may be yellow, and the like. Alternatively, red may be used as an example of the double solid line, yellow may be used as an example of the single solid line, and blue may be used as an example of the broken line. That is, instances that are determined to be the same primary name may have the same color; or instances determined to be the same secondary name have the same color, and are not limited herein. Therefore, the marked examples can be conveniently and clearly checked by the marking personnel, and the marking quality is improved.

In some embodiments, a set of attributes in the current annotation scenario may be preset by the annotator or auditor, so that the annotator determines the attributes of the annotated instance according to the preset set of attributes during the annotation process, so as to normalize the annotation process. For example, it can be determined in advance which instances need to be marked in the marking scenario. In an example, in a point cloud lane line instance labeling scene, it may be predetermined that instances to be labeled include a solid line, a dashed line, a stop line, and the like, and in the labeling process of a labeling person, it may be selected to determine whether a currently labeled instance should be a solid line, a dashed line, or a stop line. Therefore, according to the embodiment of the disclosure, a marking person does not need to manually input the corresponding instance attribute, so that the problems of duplicate names, wrongly written characters and the like are effectively solved, and the method has strong applicability and improves the marking quality.

According to some embodiments, the attributes further comprise an instance identification for distinguishing between multiple instances of the point cloud data having the same name. For example, when multiple instances of the same type exist in a point cloud instance labeling scenario, for example, in a labeling scenario including multiple street lamp instances, an instance identifier of each labeled street lamp instance may be determined. Multiple sets of instance identifications may be set to correspond to multiple types (i.e., names) of instances, respectively. For example, the instance id may be 0, 1, 2, 3 … to uniquely identify the corresponding instance with the corresponding instance name, such as street light-0, street light-1, street light-2 ….

According to some embodiments, the method 300 may further include defining a special area. The special region may be, for example, a point cloud region in the point cloud data that does not require labeling. It may be defined whether there are unmarked regions that can be committed to prevent unmarked certain regions.

In some embodiments, all regions in the point cloud data may be identified in advance as special regions, or as having special region attributes. In the marking process of the marking personnel, the area needing to be marked is marked as an example or is modified into other example attributes according to the actual situation. Thus, the region marked by the unmarked person is still the special region. If the special area is set to exist for submitting, the marking personnel can judge whether to submit the marking data at any time. If the special areas are set to exist, the special areas can not be submitted, and the special areas can be submitted only after the annotating personnel annotate all the areas in the point cloud data, so that the mark omission of some areas by the annotating personnel is prevented.

According to some embodiments, further comprising: one or more marked instances are displayed or hidden in response to receiving corresponding instructions. Illustratively, the cut point cloud data can generate a new point cluster and appear in the point cluster list, and a annotator and an auditor can select and view an instance corresponding to the corresponding point cluster in a point selection mode.

And displaying the corresponding examples, so that a marker can conveniently judge whether the corresponding marking quality is met, and the marker or an auditor can conveniently check the positions and the attributes of the marked examples. The marked examples are hidden, so that the marked examples can be prevented from influencing the marking of other point cloud areas, and marking personnel can conveniently check whether some areas are missed. In some examples, the display and hiding of the marked instances may be controlled by a shortcut key or shortcut button. Other methods of controlling the display and hiding of the auxiliary lines are possible, and are not limited herein.

According to some embodiments, the attributes of the points in the point cloud data may include intensity values. The method 300 may further include: and adjusting the intensity value range mapped to the color space based on the scene corresponding to the point cloud so that the point displays corresponding colors according to the intensity value of the point.

Exemplarily, in a point cloud lane line instance labeling scene, the intensity values of the points collected by the radar are different (for example, the value of the intensity value is 0 to 255) due to the difference of the physical materials of the lane line and the ground. The points of different intensity values can be presented differently by mapping the intensity values into a color space, e.g. the closer the intensity value is to 0 the closer the color is presented to red, the closer the intensity value is to 255 the closer the color is presented to blue. However, the actual display is not necessarily directly distinguishable, and the following statistics are given according to the past: the ground intensity value is approximately 0 and the lane line intensity value is approximately 7, such that both regions exhibit colors that are close to red as described above. According to the embodiment of the application, the intensity value range mapped to the color space can be adjusted at the client based on the scene corresponding to the point cloud. For example, prior to instance labeling of point cloud data, first adjustment is made such that the closer the intensity value is to 0, the closer the color is to red, and the closer the intensity value is to 7, the closer the color is to blue. Therefore, the ground and the lane line can be effectively distinguished, and convenience is provided for instance marking of marking personnel.

In some examples, a draggable adjustment unit or button may be provided at the client to manually draggable adjust the range of intensity values mapped to the color space based on the scene to which the point cloud corresponds. Alternatively, a mappable intensity value range value may be received on a client basis (e.g., based on an input box on a display interface). It should be understood that other ways that can be used to adjust the mappable intensity range are possible and not limiting herein.

According to an embodiment of the present disclosure, as shown in fig. 4, there is also provided a point cloud instance labeling apparatus 400, including: an obtaining unit 410 configured to obtain point cloud data to be labeled; an annotation unit 420 configured to annotate one or more instances on the point cloud data, wherein each instance comprises one or more point cloud regions in the point cloud data; and a determining unit 430 configured to determine attributes of the one or more instances, wherein the attributes are one or more of a set of attributes preset based on a scene corresponding to the point cloud.

According to some embodiments, the attribute may comprise a name. Illustratively, the name may include at least one of: the system comprises a primary name and a secondary name, wherein one or more secondary names can be included under the primary name.

According to some embodiments, instances determined to be the same name have the same visual attribute.

According to some embodiments, the visual attribute comprises a color.

According to some embodiments, the attributes further comprise an instance identification for distinguishing between multiple instances of the point cloud data having the same name.

According to some embodiments, the apparatus 400 may further include a unit defining a special area. The special area is a point cloud area which does not need to be marked in the point cloud data.

According to some embodiments, the apparatus 400 may further include means for displaying or hiding one or more marked instances in response to receiving corresponding instructions.

According to some embodiments, the attributes of the points in the point cloud data comprise intensity values. The apparatus 400 may further include: and a unit for adjusting the range of intensity values mapped to the color space based on the scene corresponding to the point cloud so that the point displays corresponding colors according to the intensity values.

Here, the operations of the above units 410 to 430 of the point cloud example labeling apparatus 400 are similar to the operations of the steps 310 to 330 described above, and are not repeated herein.

There is also provided, in accordance with an exemplary embodiment of the present disclosure, an electronic device, including: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the point cloud instance annotation method described above.

There is also provided, in accordance with an exemplary embodiment of the present disclosure, a non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to execute the point cloud instance labeling method described above.

There is also provided, in accordance with an exemplary embodiment of the present disclosure, a computer program product, including a computer program, wherein the computer program, when executed by a processor, implements the point cloud instance annotation method described above.

Referring to fig. 5, a block diagram of a structure of an electronic device 500, which may be a server or a client of the present disclosure, which is an example of a hardware device that may be applied to aspects of the present disclosure, will now be described. Electronic device is intended to represent various forms of digital electronic computer devices, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the disclosure described and/or claimed herein.

As shown in fig. 5, the apparatus 500 comprises a computing unit 501 which may perform various appropriate actions and processes in accordance with a computer program stored in a Read Only Memory (ROM)502 or a computer program loaded from a storage unit 508 into a Random Access Memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the device 500 can also be stored. The calculation unit 501, the ROM 502, and the RAM 503 are connected to each other by a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.

A number of components in the device 500 are connected to the I/O interface 505, including: an input unit 506, an output unit 507, a storage unit 508, and a communication unit 509. The input unit 506 may be any type of device capable of inputting information to the device 500, and the input unit 506 may receive input numeric or character information and generate key signal inputs related to user settings and/or function controls of the electronic device, and may include, but is not limited to, a mouse, a keyboard, a touch screen, a track pad, a track ball, a joystick, a microphone, and/or a remote controller. Output unit 507 may be any type of device capable of presenting information and may include, but is not limited to, a display, speakers, a video/audio output terminal, a vibrator, and/or a printer. The storage unit 508 may include, but is not limited to, a magnetic disk, an optical disk. The communication unit 509 allows the device 500 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunications networks, and may include, but is not limited to, modems, network cards, infrared communication devices, wireless communication transceivers and/or chipsets, such as bluetooth (TM) devices, 1302.11 devices, WiFi devices, WiMax devices, cellular communication devices, and/or the like.

The computing unit 501 may be a variety of general-purpose and/or special-purpose processing components having processing and computing capabilities. Some examples of the computing unit 501 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various dedicated Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, and so forth. The computing unit 501 performs the various methods and processes described above, such as the method 300. For example, in some embodiments, the method 300 may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as the storage unit 508. In some embodiments, part or all of the computer program may be loaded and/or installed onto the device 500 via the ROM 502 and/or the communication unit 509. When loaded into RAM 503 and executed by the computing unit 501, may perform one or more of the steps of the method 300 described above. Alternatively, in other embodiments, the computing unit 501 may be configured to perform the method 300 by any other suitable means (e.g., by means of firmware).

Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.

Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), and the Internet.

The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.

It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present disclosure may be performed in parallel, sequentially or in different orders, and are not limited herein as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved.

Although embodiments or examples of the present disclosure have been described with reference to the accompanying drawings, it is to be understood that the above-described methods, systems and apparatus are merely exemplary embodiments or examples and that the scope of the present invention is not limited by these embodiments or examples, but only by the claims as issued and their equivalents. Various elements in the embodiments or examples may be omitted or may be replaced with equivalents thereof. Further, the steps may be performed in an order different from that described in the present disclosure. Further, various elements in the embodiments or examples may be combined in various ways. It is important that as technology evolves, many of the elements described herein may be replaced with equivalent elements that appear after the present disclosure.

Claims

1. A point cloud instance labeling method comprises the following steps:

acquiring point cloud data to be marked;

labeling one or more instances on the point cloud data, wherein each of the instances comprises one or more point cloud regions in the point cloud data; and

determining attributes of the one or more instances, wherein the attributes are one or more of a set of attributes preset based on a scene corresponding to the point cloud.

2. The method of claim 1, wherein the attribute comprises a name, wherein the name comprises at least one of: the system comprises a primary name and a secondary name, wherein the primary name comprises one or more secondary names.

3. The method of claim 2, wherein instances determined to be the same name have the same visual attribute.

4. The method of claim 3, wherein the visual attribute comprises color.

5. The method of claim 2, wherein the attributes further comprise an instance identification for distinguishing between multiple instances of the point cloud data having the same name.

6. The method of any of claims 1 to 4, further comprising: defining a special area, wherein the special area is a point cloud area which does not need to be marked in the point cloud data.

7. The method of any of claims 1 to 4, further comprising: one or more marked instances are displayed or hidden in response to receiving corresponding instructions.

8. The method of any of claims 1 to 4, wherein the attributes of the points in the point cloud data comprise intensity values, wherein the method further comprises:

and adjusting the intensity value range mapped to the color space based on the scene corresponding to the point cloud so that the point displays corresponding colors according to the intensity value of the point.

9. A point cloud instance annotation device, comprising:

the acquisition unit is configured to acquire point cloud data to be marked;

a labeling unit configured to label one or more instances on the point cloud data, wherein each of the instances comprises one or more point cloud areas in the point cloud data; and

the determining unit is configured to determine attributes of the one or more instances, wherein the attributes are one or more of a set of attributes preset based on a scene corresponding to the point cloud.

10. The apparatus of claim 9, wherein the attribute comprises a name, wherein the name comprises at least one of: the system comprises a primary name and a secondary name, wherein the primary name comprises one or more secondary names.

11. The apparatus of claim 10, wherein instances determined to be the same name have the same visual attribute.

12. The apparatus of claim 11, wherein the visual attribute comprises a color.

13. The apparatus of claim 10, wherein the attributes further comprise an instance identification for distinguishing between multiple instances of the point cloud data having the same name.

14. The apparatus of any of claims 9 to 13, further comprising: and defining a unit of a special area, wherein the special area is a point cloud area which does not need to be marked in the point cloud data.

15. The apparatus of any of claims 9 to 13, further comprising: means for displaying or hiding one or more marked instances in response to receiving a corresponding instruction.

16. The apparatus of any of claims 9 to 13, wherein the attributes of the points in the point cloud data comprise intensity values, further comprising:

and a unit for adjusting the range of intensity values mapped to the color space based on the scene corresponding to the point cloud so that the point displays corresponding colors according to the intensity values.

17. An electronic device, comprising:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein

The memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-8.

18. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1-8.

19. A computer program product comprising a computer program, wherein the computer program realizes the method of any one of claims 1-8 when executed by a processor.